Kalman filtering in pairwise Markov trees

François Desbouvries; Jean Lecomte; Wojciech Pieczynski

doi:10.1016/j.sigpro.2005.07.026

Kalman filtering in pairwise Markov trees

CNRS UMR 5157 SAMOVAR

Research output: Contribution to journal › Article › peer-review

Abstract

An important problem in multiresolution analysis of signals or images consists in estimating hidden random variables x={xs}s∈S from observed ones y={ys}s∈S. This is done classically in the context of hidden Markov trees (HMT). HMT have been extended recently to the more general context of pairwise Markov trees (PMT). In this note, we propose an adaptive filtering algorithm which is an extension to PMT of the Kalman filter (KF).

Original language	English
Pages (from-to)	1049-1054
Number of pages	6
Journal	Signal Processing
Volume	86
Issue number	5
DOIs	https://doi.org/10.1016/j.sigpro.2005.07.026
Publication status	Published - 1 Jan 2006

Keywords

Hidden Markov trees
Kalman filtering
Multiresolution signal and image analysis
Multiscale algorithms
Pairwise Markov trees

Access to Document

10.1016/j.sigpro.2005.07.026

Cite this

@article{70c015d71cc44e5ca0bc6dc665aa0f8e,

title = "Kalman filtering in pairwise Markov trees",

abstract = "An important problem in multiresolution analysis of signals or images consists in estimating hidden random variables x=\{xs\}s∈S from observed ones y=\{ys\}s∈S. This is done classically in the context of hidden Markov trees (HMT). HMT have been extended recently to the more general context of pairwise Markov trees (PMT). In this note, we propose an adaptive filtering algorithm which is an extension to PMT of the Kalman filter (KF).",

keywords = "Hidden Markov trees, Kalman filtering, Multiresolution signal and image analysis, Multiscale algorithms, Pairwise Markov trees",

author = "Fran{\c c}ois Desbouvries and Jean Lecomte and Wojciech Pieczynski",

year = "2006",

month = jan,

day = "1",

doi = "10.1016/j.sigpro.2005.07.026",

language = "English",

volume = "86",

pages = "1049--1054",

journal = "Signal Processing",

issn = "0165-1684",

number = "5",

}

TY - JOUR

T1 - Kalman filtering in pairwise Markov trees

AU - Desbouvries, François

AU - Lecomte, Jean

AU - Pieczynski, Wojciech

PY - 2006/1/1

Y1 - 2006/1/1

N2 - An important problem in multiresolution analysis of signals or images consists in estimating hidden random variables x={xs}s∈S from observed ones y={ys}s∈S. This is done classically in the context of hidden Markov trees (HMT). HMT have been extended recently to the more general context of pairwise Markov trees (PMT). In this note, we propose an adaptive filtering algorithm which is an extension to PMT of the Kalman filter (KF).

AB - An important problem in multiresolution analysis of signals or images consists in estimating hidden random variables x={xs}s∈S from observed ones y={ys}s∈S. This is done classically in the context of hidden Markov trees (HMT). HMT have been extended recently to the more general context of pairwise Markov trees (PMT). In this note, we propose an adaptive filtering algorithm which is an extension to PMT of the Kalman filter (KF).

KW - Hidden Markov trees

KW - Kalman filtering

KW - Multiresolution signal and image analysis

KW - Multiscale algorithms

KW - Pairwise Markov trees

U2 - 10.1016/j.sigpro.2005.07.026

DO - 10.1016/j.sigpro.2005.07.026

M3 - Article

AN - SCOPUS:33644606607

SN - 0165-1684

VL - 86

SP - 1049

EP - 1054

JO - Signal Processing

JF - Signal Processing

IS - 5

ER -

Kalman filtering in pairwise Markov trees

Abstract

Keywords

Access to Document

Fingerprint

Cite this